GAS SOURCE LOCALIZATION THROUGH DEEP LEARNING METHOD BASED ON GAS DISTRIBUTION MAP DATABASE

Authors

  • Zaffry Hadi Mohd Juffry Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau Perlis Malaysia https://orcid.org/0000-0002-6012-8810
  • Kamarulzaman Kamarudin Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau Perlis Malaysia https://orcid.org/0000-0001-7764-0821
  • Abdul Hamid Adom Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau Perlis Malaysia https://orcid.org/0000-0002-2064-7157
  • Muhammad Fahmi Miskon Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka Malaysia
  • Ahmad Shakaff Ali Yeon Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau Perlis Malaysia
  • Abdulnasser Nabil Abdullah Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau Perlis Malaysia

DOI:

https://doi.org/10.11113/jurnalteknologi.v86.20186

Keywords:

Gas source localization, gas distribution map, deep learning, harmful gas dispersion, mobile robot olfaction

Abstract

The incident of harmful gas leakage can cause severe damage to the environment and several casualties to human beings while the gas localization system plays a major role in mitigating those causalities. With the advances in artificial intelligence technology, deep learning is able to enhance the accuracy of the gas localization system to locate the gas source. This paper proposes a gas localization system that utilizes three different deep learning models namely DNN, 1DCNN, and 2DCNN to locate the gas source within the gas map. The proposed method involves generating the gas distribution map through the large gas sensor array platform in real-world indoor scenarios. Those models are then trained using the collected database which allows for accurate prediction of the gas source location. The performance of each proposed deep learning model was compared to find the best model demonstrating the highest effectiveness in identifying gas leaks. The study has shown that the 1DCNN has the highest effectiveness in predicting the gas source in the range between 0.0 m to 0.3 m with 90.3% compared to the DNN and 2DCNN models.

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Published

2024-01-16

How to Cite

Mohd Juffry, Z. H., Kamarudin, K., Adom, A. H., Miskon, M. F., Ali Yeon, A. S., & Abdullah, A. N. (2024). GAS SOURCE LOCALIZATION THROUGH DEEP LEARNING METHOD BASED ON GAS DISTRIBUTION MAP DATABASE. Jurnal Teknologi, 86(2), 199–208. https://doi.org/10.11113/jurnalteknologi.v86.20186

Issue

Section

Science and Engineering